Scatter Search

Scatter search This chapter discusses the principles and foundations behind scatter search and its application to the problem of training neural networks. Scatter search is an evolutionary method that has been successfully applied to a wide array of hard optimization problems. Scatter search constructs new trial solutions by combining so-called reference solutions and employing strategic designs that exploit context knowledge. In contrast to other evolutionary methods like genetic algorithms, scatter search is founded on the premise that systematic designs and methods for creating new solutions afford significant benefits beyond those derived from recourse to randomization. Our implementation goal is to create a combination of the five elements in the scatter search methodology that proves effective when searching for optimal weight values in a multilayer neural network. Through experimentation, we show that our instantiation of scatter search can compete with the best-known training algorithms in terms of training quality while keeping the computational effort at a reasonable level. (Source: http://plato.asu.edu)


References in zbMATH (referenced in 297 articles , 1 standard article )

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  1. Carvalho, Desiree M.; Nascimento, Mariá C. V.: Hybrid matheuristics to solve the integrated lot sizing and scheduling problem on parallel machines with sequence-dependent and non-triangular setup (2022)
  2. Kardam, Yogita Singh; Srivastava, Kamal; Jain, Pallavi; Martí, Rafael: Scatter search for the minimum leaf spanning tree problem (2022)
  3. Liu, Xiaolu; Chen, Jiaming; Wang, Minghui; Wang, Yang; Su, Zhouxing; Lü, Zhipeng: A two-phase tabu search based evolutionary algorithm for the maximum diversity problem (2022)
  4. Todosijević, Raca; Hanafi, Saïd; Glover, Fred: On convergence of scatter search and star paths with directional rounding for 0--1 mixed integer programs (2022)
  5. Cho, Wendy K. Tam; Liu, Yan Y.: A parallel evolutionary multiple-try Metropolis Markov chain Monte Carlo algorithm for sampling spatial partitions (2021)
  6. Kain, Raslan; Manerba, Daniele; Tadei, Roberto: The index selection problem with configurations and memory limitation: a scatter search approach (2021)
  7. López-Sánchez, A. D.; Sánchez-Oro, J.; Laguna, M.: A new scatter search design for multiobjective combinatorial optimization with an application to facility location (2021)
  8. Máximo, Vinícius R.; Nascimento, Mariá C. V.: A hybrid adaptive iterated local search with diversification control to the capacitated vehicle routing problem (2021)
  9. Riahi, Vahid; Hakim Newton, M. A.; Sattar, Abdul: A scatter search algorithm for time-dependent prize-collecting arc routing problems (2021)
  10. Alcaraz, Javier; García-Nové, Eva M.; Landete, Mercedes; Monge, Juan F.: The linear ordering problem with clusters: a new partial ranking (2020)
  11. Li, Xun; Wu, Dandan; He, Jingjing; Bashir, Muhammad; Liping, Ma: An improved method of particle swarm optimization for path planning of mobile robot (2020)
  12. Mahmoodjanloo, Mehdi; Tavakkoli-Moghaddam, Reza; Baboli, Armand; Jamiri, Atefeh: A multi-modal competitive hub location pricing problem with customer loyalty and elastic demand (2020)
  13. Wu, Qinghua; Wang, Yang; Glover, Fred: Advanced tabu search algorithms for bipartite Boolean quadratic programs guided by strategic oscillation and path relinking (2020)
  14. Barbosa, Flávia; Berbert Rampazzo, Priscila C.; Yamakami, Akebo; Camanho, Ana S.: The use of frontier techniques to identify efficient solutions for the berth allocation problem solved with a hybrid evolutionary algorithm (2019)
  15. Eskandarpour, Majid; Ouelhadj, Djamila; Hatami, Sara; Juan, Angel A.; Khosravi, Banafsheh: Enhanced multi-directional local search for the bi-objective heterogeneous vehicle routing problem with multiple driving ranges (2019)
  16. Glover, Fred; Kochenberger, Gary; Du, Yu: Quantum bridge analytics. I: A tutorial on formulating and using QUBO models (2019)
  17. Kar, Mohuya B.; Kar, Samarjit; Guo, Sini; Li, Xiang; Majumder, Saibal: A new bi-objective fuzzy portfolio selection model and its solution through evolutionary algorithms (2019)
  18. Stefanello, Fernando; Aggarwal, Vaneet; Buriol, Luciana S.; Resende, Mauricio G. C.: Hybrid algorithms for placement of virtual machines across geo-separated data centers (2019)
  19. Vallada, Eva; Villa, Fulgencia; Fanjul-Peyro, Luis: Enriched metaheuristics for the resource constrained unrelated parallel machine scheduling problem (2019)
  20. Xu, Zhenxing; He, Kun; Li, Chu-Min: An iterative path-breaking approach with mutation and restart strategies for the MAX-SAT problem (2019)

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